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@rohitg00
rohitg00 / llm-wiki.md
Last active April 22, 2026 00:47 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

@ebta
ebta / docker-install.md
Last active April 22, 2026 00:44
Installing docker (latest version) on Ubuntu 20.04

Installing docker on Ubuntu 20.04 / 22.04

Full reference here: https://docs.docker.com/engine/install/ubuntu/

Setup the repository

sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates curl gnupg lsb-release

Add Docker’s official GPG key:

@FadeMind
FadeMind / fuck_telemetry.cmd
Created August 26, 2020 13:24
Turn Off Telemetry in Windows 10
@echo off
echo.
openfiles > NUL 2>&1
if %errorlevel% NEQ 0 (
echo You are not running as Administrator...
echo This batch cannot do it's job without elevation!
echo.
echo Right-click and select ^'Run as Administrator^' and try again...
echo.
@inntran
inntran / apple-background-sounds.md
Last active April 22, 2026 00:43
Apple Background Sounds
@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@win3zz
win3zz / GitHub-Leaked-API-Keys-and-Secrets.md
Last active April 22, 2026 00:29

GitHub Search Syntax for Finding API Keys/Secrets/Tokens

As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.

Search Syntax:

(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))

Examples:

**1.